• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.156-166
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• 2000

A rate-distortion based segmentation using recursive merging is presented, which considers texture as a homogeneity by adopting the procedure of a generalized texture approximation. The texture in a region is approximated by SA-DCT and a set of two uniform quantizers with fixed step sizes, one for DC and another for AC. Using the approximated texture, we calculated the rate-distortion based cost. The segmentation using recursive merging is performed by using the rate-distortion based cost. Experimental results for 256$\times$256 Lena show that the region-based coding using the proposed segmentation yields the PSNR improvements of 0.8~ 1.0 dB and 1.2~1.5 dB over that using the rate-distortion based segmentation with DC approximation only and JPEG, respectively.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1995-1996
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• 2008

한의학에서 안면은 인체 내부의 생리적, 병리적 변화를 반영하는 중요한 기관으로 망진을 통해 얻은 안면으로부터의 정보는 진단의 주요 요소로 이용되고 있다. 하지만 안면 진단의 편리함과 비침습적인 특성 및 결과의 효용성에도 불구하고 진단의 객관화, 표준화 문제로 인해 널리 사용되지 못하고 있다. 따라서 본 논문에서는 안면 진단의 정량화, 과학화 연구의 일환으로서, 표준화 된 안면 영상을 획득하고 주변환경 및 광원의 영향을 최소화 할 수 있는 색상 보정 알고리즘을 제안한다. 또한, 보정된 영상을 이용하여 진단에 필요한 안면 영역을 추출하고 영역을 분할하여 각 분할된 안면 영역별 컬러 특징 및 변화 정도를 수치적으로 확인할 수 있는 알고리즘을 함께 제안하고 있다. 이는 자동화된 안면 진단 시스템을 만들기 위한 선행연구로서의 의미를 가지며, 객관적이고 표준화된 안면 진단을 가능하게 할 것이다.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.305-308
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• 2000

암의 진행 정도를 판단하기 위한 암세포 조직영상의 분석은 그 대상이 되는 영상의 다양성과 잡음으로 인해 정확한 분석이 어렵다. 특히, 암의 진행 정도를 판단하는데 있어서 중요한 요인인 세포핵의 variation에 따른 order/disorder 정도를 객관적 수치로 정량화하기 위해서는, 각 기(stage)에 따른 암의 진행정도를 가장 잘 나타낼 수 있는 특징값 추출이 필수적이다. 본 논문에서는 가장 유효한 특징값을 추출하기 위하여, 공간 영역과 주파수 영역에서 그 지역적 특징을 잘 나타내는 wavelet 변환을 적용한 후, 분할 된 서브 밴드 중 고대역 서브 밴드에서 질감 특징을 추출하고, 추출 된 질감 특징값들이 암의 진행 정도에 따른 각 집단간에 유의한 차이를 나타내는지에 대한 유의성을 검증하기 위하여, 다변량 통계학적 분석 방법을 사용하여 비교분석 하였다.

• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.45-55
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• 2013

In this study, we propose a tongue diagnosis system which detects the tongue from face image and divides the tongue area into six areas, and finally generates tongue fur ratio of each area. To detect the tongue area from face image, we use ASM as one of the active shape models. Detected tongue area is divided into six areas and the distribution of tongue coating of six areas is examined by SVM. For SVM, we use a 3-dimensional vector calculated by PCA from a 12-dimensional vector consisting of RGB, HSV, Lab, and Luv. As a result, we stably detected the tongue area using ASM. Furthermore, we recognized that PCA and SVM helped to raise the ratio of tongue coating detection.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.239-246
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• 2005

Biologist must have to do 2DGE biological experiment for Protein Search and Analysis. This experiment coming into being 2 dimensional image. 2DGE (2D Gel Electrophoresis : two dimensional gel electrophoresis) image is the most widely used method for isolating of the objective protein by comparative analysis of the protein spot pattern in the gel plane. The process of protein spot analysis, firstly segment protein spots that are spread in 2D gel plane by image processing and can find important protein spots through comparative analysis with protein pattern of contrast group. In the algorithm which detect protein spots, previous 2DGE image analysis is applies gaussian fitting, however recently Watersheds region based segmentation algorithm, which is based on morphological segmentation is applied. Watersheds has the benefit that segment rapidly needed field in big sized image, however has under-segmentation and over-segmentation of spot area when gray level is continuous. The drawback was somewhat solved by marker point institution, but needs the split and merge process. This paper introduces a novel marker search of protein spots by watersheds-based hierarchical threshold, which can resolve the problem of marker-driven watersheds.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.460-463
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• 2003

의료영상에서의 혈관의 분할은 심혈관계질환의 진단 및 시술을 위한 3차원 가시화 및 가상내시경을 하기위한 필수 선행 단계로 이에 대한 연구가 많이 이루어 지고 있다. 조영제를 투여한 환자의 CT데이터에서 혈관분할의 가장 큰 문제점은 혈관의 밝기값이 뼈의 밝기값과 비슷하기 때문에 기존의 3차원 SRG방법으로 분할하는 경우 새나감의 문제를 가지고 있었다. 본 논문에서는 Cubic SRG라는 방법을 통해 기존의 3차원 SRG가 가지는 깔끔한 분할결과와 적응적인 특성등의 여러 장점을 그대로 취하며 Cubic이라는 구조적 특징을 이용하여 혈관을 빠르고 강인하게 분할하는 방법을 제안한다. Cubic SRG는 SRG가 픽셀단위의 성장을 통해 동질 영역을 분할하는 방법을 사용함에 반해 Cubic이라는 부피 단위를 지정하여 이를 SRG의 픽셀과 같이 퍼트리는 방식으로 기존의 3차원 SRG에 비해 2$\sim$5배 정도의 빠른 수행속도를 보이며 3차원 SRG의 장점인 적응적인 특성을 그대로 가질수 있도륵 구현되었다. 또한 복셀들을 Cubic이라는 단위로 묶음으로서 혈관의 구조적인 분석을 수행하여 혈관을 트리형태의 구조로 그룹화가 가능하기 때문에 혈관을 가지별로 분할하기에 용이한 특징을 가지도록 하였으며, 이를 통해 새나감이 시작된 가지를 찾아서 잘라내는 방법을 통하여 SRG의 가장 큰 문제인 새나감 방법을 효과적으로 해결하는 방법을 제시한다. 최종적으로 위의 방법을 기본으로 하여 적응형 임계값 기반의 분할 방법을 혼합하여 사용자가 지정한 두 지점사이의 혈관을 강인하게 분할할수 있도록 구현하였고, 제안한 방법으로 여러 환자의 CT데이터에 실험하여 좋은 결과를 얻을 수 있었다.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.43-51
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• 2006

Segmentation method using RSST has many advantages in extracting of accurate region boundaries and controlling the resolution of segmented result and so on. In this paper, we propose three fast RSST algorithms for image segmentation. In first method, we classify links according to weight size for fast link search. In the second method, very similar links before RSST construction are eliminated. In third method, the links of very small regions which are not important for human eye are eliminated. As a result, the total times elapsed for segmentation are reduced by about 10 $\sim$ 40 times, and reconstructed images based on the segmentation results show little degradation of PSNR and visual quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.545-554
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• 2019

This research aims to provide a building area extraction approach over the areas where data acquisition is impossible through field surveying, aerial photography and lidar scanning. Hence, high-resolution satellite images, which have high accessibility over the earth, are utilized for the automated building extraction in this study. 3D point clouds or DSM (Digital Surface Models), derived from the stereo image matching process, provides low quality of building area extraction due to their high level of noises and holes. In this regards, this research proposes a hybrid building area extraction approach which utilizes 3D point clouds (from image matching), and color and linear information (from imagery). First of all, ground and non-ground points are separated from 3D point clouds; then, the initial building hypothesis is extracted from the non-ground points. Secondly, color based building hypothesis is produced by considering the overlapping between the initial building hypothesis and the color segmentation result. Afterwards, line detection and space partitioning results are utilized to acquire the final building areas. The proposed approach shows 98.44% of correctness, 95.05% of completeness, and 1.05m of positional accuracy. Moreover, we see the possibility that the irregular shapes of building areas can be extracted through the proposed approach.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.163-170
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• 2013

In general, images of outdoor scenes often contain degradation due to dust, water drop, haze, fog, smoke and so on, as a result they cause the contrast reduction and color fading. Haze removal is not easier problem due to the inherent ambiguity between the haze and the underlying scene. So, we propose a novel method to solve single scene dehazing problem using the region segmentation based on graph algorithm that has used a gradient value as a cost function. We segment the scene into different regions according to depth-related information and then estimate the global atmospheric light. The medium transmission can be directly estimated by the threshold function of graph-based segmentation algorithm. After estimating the medium transmission, we can restore the haze-free scene. We evaluated the degree of the visibility restoration between the proposed method and the existing methods by calculating the gradient of the edge between the restored scene and the original scene. Results on a variety of outdoor haze scene demonstrated the powerful haze removal and enhanced image quality of the proposed method.